The automatic reassembling of archaeological artefacts from a collection of fragments is a crucial problem in archaeology. It is arduous and time-consuming because the available information, in the form of fragments, is limited and "noisy". Previous research to assist in reassembly of artefacts has largely focused on either pattern-recognition or augmented-visualisation based perspectives. This paper presents a computer-aided and collaborative system for the reconstruction of archaeological artefacts, using boundarymatching estimation by string registration. The system has three key components. It uses invariant features to represent the 3D boundary curves of fragments. It utilises robust string matching to search the globally optimal alignment so as to tolerate noise. To further handle limited and noisy information, it creates a collaborative environment to allow multiple archaeologists to remotely reassemble artefacts at the same time. A series of experiments verify the acceptable performance of the system as well as its components.

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Many tracking problems can be efficiently solved by the filtering technique. Linear filter methods (e.g.Kalman Filter) have shown their success and optimal ity in many linear settings with Gaussian noises. How ever, they expose inefficiency and weakness in the gen eral nonlinear and high dimensional setting (e.g. hu man tracking). While, the advancement of Sequential Importance Re-sampling with Simulated Annealing has shown it is capable of handling nonlinearity and high dimensionality of human tracking. However, its per formance is often affected by lighting variations and noises from silhouette segmentation. The proposed ap proach incorporates a textured human body template to annealed sequential filtering, and uses the illumina tion invariant CIELab formula to evaluate the obser vation likelihood so that influences of lighting changes and noises are minimised. Experiments with the bench mark HumanEvaI dataset demonstrate encouraging improvements over traditional Sequential Importance Re-sampling and the silhouette based method.

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Formation lithology identification is an indispensable link in oil and gas exploration. Precision of the traditional